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English(EN) LATCH: LLM-Assisted Timing Closure Harness for Post-Route FPGA Optimisation

LLM辅助框架改进FPGA定时闭合

研究人员开发了LATCH,一个使用大型语言模型(LLMs)辅助后布线现场可编程门阵列(FPGAs)定时闭合的框架。研究比较了不同级别的LLM控制,发现专家编写的配方比直接EDA操作(71.4%)或LLM构建的过程(17.9%)具有更高的功能通过率(98.2%)。进一步的实验表明,分支探索和知识库引导的探索显著提高了最大频率增益,知识库方法达到了20.3%。 AI

影响 这项研究展示了LLM在硬件设计中的一种新颖应用,有望加速优化过程并提高FPGA开发的效率。

排序理由 学术论文,详细介绍了使用LLM进行FPGA优化的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM辅助框架改进FPGA定时闭合

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学术论文,详细介绍了使用LLM进行FPGA优化的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shu Gu, Matthew Youngman, Yuqin Zhao, Cristian Sestito, Ali Siddique, Ramesh Krishnamurthy, Danial Chitnis, Themis Prodromakis ·

    LATCH:LLM辅助时序收敛工具,用于布线后FPGA优化

    arXiv:2610.16625v1 Announce Type: new Abstract: Post-route FPGA timing closure remains expert-dependent and computationally expensive. We present LATCH (LLM-Assisted Timing Closure Harness), a two-layer framework that separates large language model (LLM) orchestration from determ…